# A Transferability Metric Using Scene Similarity and Local Map Observation for DRL Navigation

arXiv: https://arxiv.org/abs/2306.04910 · HTML: https://mlsanity.com/paper/2306.04910/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jun 2023.

## Built on by (most cited first, 2 of 2)

- [Spatial-VLN: Zero-Shot Vision-and-Language Navigation With Explicit Spatial Perception and Exploration](https://mlsanity.com/paper/2601.12766/) (2026)
- [EmoBipedNav: Emotion-aware Social Navigation for Bipedal Robots with Deep Reinforcement Learning](https://mlsanity.com/paper/2503.12538/) (2025)

## Abstract (arXiv)

While deep reinforcement learning (DRL) has attracted a rapidly growing interest in solving the problem of navigation without global maps, DRL typically leads to a mediocre navigation performance in practice due to the gap between the training scene and the actual test scene. To quantify the transferability of a DRL agent between the training and test scenes, this paper proposes a new transferability metric -- the scene similarity calculated using an improved image template matching algorithm. Specifically, two transferability performance indicators are designed including the global scene similarity that evaluates the overall robustness of a DRL algorithm and the local scene similarity that serves as a safety measure when a DRL agent is deployed without a global map. In addition, this paper proposes the use of a local map that fuses 2D LiDAR data with spatial information of both the agent and the destination as the DRL observation, aiming to improve the transferability of DRL navigation algorithms. With a wheeled robot as the case study platform, both simulation and real-world experiments are conducted in a total of 26 different scenes. The experimental results affirm the robustness of the local map observation design and demonstrate the strong correlation between the scene similarity metric and the success rate of DRL navigation algorithms.